Storing feature sets using semi-structured data storage

ABSTRACT

The subject technology receives, by a database system, raw input data from a source table provided by a machine learning development environment, the source table comprising multiple rows where each row includes multiple columns, the raw input data comprising values in a first format, the values comprising input features corresponding to datasets included in the raw input data for machine learning models, the machine learning development environment comprising an external system from the database system and is accessed by a plurality of different users that are external to the database system. The subject technology generates cell data for a feature store table based at least in part on the values from the source table. The subject technology performs at least one database operation to generate the feature store table including at least table metadata, column metadata, and the generated cell data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/390,883, filed Jul. 31, 2021, entitled “SEMI-STRUCTURED DATA STORAGEAND PROCESSING FUNCTIONALITY TO STORE SPARSE FEATURE SETS,” which claimspriority to U.S. Provisional Patent Application Ser. No. 63/202,192,filed May 31, 2021, entitled “SEMI-STRUCTURED DATA STORAGE ANDPROCESSING FUNCTIONALITY TO STORE SPARSE FEATURE SETS,” and the contentsof each of which are incorporated herein by reference in theirentireties for all purposes.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to a network-baseddatabase system or a cloud data platform and, more specifically, toprocessing and storing data for machine learning models within thedatabase system.

BACKGROUND

Cloud-based data warehouses and other database systems or data platformsare often utilized for developing machine learning models andapplications that leverage such models. However, such systems may havedifficulty in processing datasets for machine learning due to inherentstructures or formats for storing such datasets which can reducecomputational efficiency and increase storage utilization.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based data warehouse system in communication with a cloudstorage platform, in accordance with some embodiments of the presentdisclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is a computing environment conceptually illustrating an examplesoftware architecture, in accordance with some embodiments of thepresent disclosure.

FIG. 5 illustrates examples of database tables, in accordance with someembodiments of the present disclosure.

FIG. 6 illustrates examples of database tables, in accordance with someembodiments of the present disclosure.

FIG. 7 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 8 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

Machine learning (ML) has seen a rise in popularity in recent years dueto the availability of massive amounts of raw input data, and advancesin more powerful and efficient computing hardware. Machine learning mayutilize models that are executed to provide predictions in particularapplications.

A machine learning lifecycle may include the following distinct stages:data collection, annotation, exploration, feature engineering,experimentation, training, evaluation, and deployment. The machinelearning lifecycle can be iterative from data collection throughevaluation. At each stage, any prior stage could be revisited, and eachstage can also change the size and shape of the data used to generatethe ML model.

A machine learning development lifecycle may be highly-iterative andexperimental. For example, experiments involving tens or hundreds ofinput features and model parameters may be required to produce anaccurate and well-calibrated ML model. In an example, a team of users(e.g., developers) conduct experiments and tests with many data inputs,often in both local and distributed (e.g., networked) computingenvironments such as a machine learning development environment.

In some examples, data and physical sciences or technology areas oftengenerate datasets for storage and analysis that are “tall” (e.g., basedon a number of multiple rows/observations), and also very wide (based ona number of multiple columns/features). Examples of such datasetsinclude applications for genome base-pairs, recommendation-systems(customer×purchase matrix), fraud detection, classification, and thelike. Such datasets therefore can present challenges for storage andprocessing in conventional relational database systems. In particular,very wide datasets can go beyond physical table processing and storagelimits of such relational database tables as supported in suchrelaxational database systems. In an example, these issues may be solvedutilizing file-stores and clustered high-performance computingenvironments using various forms of sparse-matrix representations.Moreover, such challenges, and the limitations of conventionalrelational-database systems have also popularized the data-lake (e.g.,file-store) for data/physical science processing.

As described further herein, the subject technology provides techniques(e.g., sparse-matrix/key-value) to model the data values as “cells”making extensive use of semi-structured data processing and storagefeatures to store the data-values, and their metadata (e.g., table,column, row level) in a single table using single variant(semi-structured) column for maximum application flexibility.Advantageously, in some implementations, embodiments of the subjecttechnology provide considerable flexibility to implement standardpattern based approaches for selective data-retrieval, sparse (e.g.,null/zero) value imputation, common transformations, and sparse-matrixcomputation methods via SQL and UDFs (e.g., user defined functions)inside the database, without the need for data extraction or externaldata-processing.

FIG. 1 illustrates an example computing environment 100 that includes adatabase system in the example form of a network-based database system102, in accordance with some embodiments of the present disclosure. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 1 . However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of thecomputing environment 100 to facilitate additional functionality that isnot specifically described herein. In other embodiments, the computingenvironment may comprise another type of network-based database systemor a cloud data platform.

As shown, the computing environment 100 comprises the network-baseddatabase system 102 in communication with a cloud storage platform 104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage),and a cloud credential store provider 106. The network-based databasesystem 102 is a network-based system used for reporting and analysis ofintegrated data from one or more disparate sources including one or morestorage locations within the cloud storage platform 104. The cloudstorage platform 104 comprises a plurality of computing machines andprovides on-demand computer system resources such as data storage andcomputing power to the network-based database system 102.

The network-based database system 102 comprises a compute servicemanager 108, an execution platform 110, and one or more metadatadatabases 112. The network-based database system 102 hosts and providesdata reporting and analysis services to multiple client accounts.

The compute service manager 108 coordinates and manages operations ofthe network-based database system 102. The compute service manager 108also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (alsoreferred to as “virtual warehouses”). The compute service manager 108can support any number of client accounts such as end users providingdata storage and retrieval requests, system administrators managing thesystems and methods described herein, and other components/devices thatinteract with compute service manager 108.

The compute service manager 108 is also in communication with a clientdevice 114. The client device 114 corresponds to a user of one of themultiple client accounts supported by the network-based database system102. A user may utilize the client device 114 to submit data storage,retrieval, and analysis requests to the compute service manager 108.

The compute service manager 108 is also coupled to one or more metadatadatabases 112 that store metadata pertaining to various functions andaspects associated with the network-based database system 102 and itsusers. For example, a metadata database 112 may include a summary ofdata stored in remote data storage systems as well as data availablefrom a local cache. Additionally, a metadata database 112 may includeinformation regarding how data is organized in remote data storagesystems (e.g., the cloud storage platform 104) and the local caches.Information stored by a metadata database 112 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

As another example, a metadata database 112 can store one or morecredential objects 115. In general, a credential object 115 indicatesone or more security credentials to be retrieved from a remotecredential store. For example, the credential store provider 106maintains multiple remote credential stores 118-1 to 118-N. Each of theremote credential stores 118-1 to 118-N may be associated with a useraccount and may be used to store security credentials associated withthe user account. A credential object 115 can indicate one of moresecurity credentials to be retrieved by the compute service manager 108from one of the remote credential stores 118-1 to 118-N (e.g., for usein accessing data stored by the storage platform 104).

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to storage platform 104 of the cloud storage platform104. The storage platform 104 comprises multiple data storage devices120-1 to 120-N. In some embodiments, the data storage devices 120-1 to120-N are cloud-based storage devices located in one or more geographiclocations. For example, the data storage devices 120-1 to 120-N may bepart of a public cloud infrastructure or a private cloud infrastructure.The data storage devices 120-1 to 120-N may be hard disk drives (HDDs),solid state drives (SSDs), storage clusters, Amazon S3™ storage systems,or any other data storage technology. Additionally, the cloud storageplatform 104 may include distributed file systems (such as HadoopDistributed File Systems (HDFS)), object storage systems, and the like.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete cache files using a least recently used (LRU) policy andimplement an out of memory (00M) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

The compute service manager 108, metadata database(s) 112, executionplatform 110, and storage platform 104, are shown in FIG. 1 asindividual discrete components. However, each of the compute servicemanager 108, metadata database(s) 112, execution platform 110, andstorage platform 104 may be implemented as a distributed system (e.g.,distributed across multiple systems/platforms at multiple geographiclocations). Additionally, each of the compute service manager 108,metadata database(s) 112, execution platform 110, and storage platform104 can be scaled up or down (independently of one another) depending onchanges to the requests received and the changing needs of thenetwork-based database system 102. Thus, in the described embodiments,the network-based database system 102 is dynamic and supports regularchanges to meet the current data processing needs.

During typical operation, the network-based database system 102processes multiple jobs determined by the compute service manager 108.These jobs are scheduled and managed by the compute service manager 108to determine when and how to execute the job. For example, the computeservice manager 108 may divide the job into multiple discrete tasks (ortransactions as discussed further herein) and may determine what data isneeded to execute each of the multiple discrete tasks. The computeservice manager 108 may assign each of the multiple discrete tasks toone or more nodes of the execution platform 110 to process the task. Thecompute service manager 108 may determine what data is needed to processa task and further determine which nodes within the execution platform110 are best suited to process the task. Some nodes may have alreadycached the data needed to process the task and, therefore, be a goodcandidate for processing the task. Metadata stored in a metadatadatabase 112 assists the compute service manager 108 in determiningwhich nodes in the execution platform 110 have already cached at least aportion of the data needed to process the task. One or more nodes in theexecution platform 110 process the task using data cached by the nodesand, if necessary, data retrieved from the cloud storage platform 104.It is desirable to retrieve as much data as possible from caches withinthe execution platform 110 because the retrieval speed is typically muchfaster than retrieving data from the cloud storage platform 104.

As shown in FIG. 1 , the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices120-1 to 120-N in the cloud storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 120-1 to 120-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the cloud storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , the compute service manager 108includes an access manager 202 and a credential management system 204coupled to an access metadata database 206, which is an example of themetadata database(s) 112. Access manager 202 handles authentication andauthorization tasks for the systems described herein. The credentialmanagement system 204 facilitates use of remote stored credentials(e.g., credentials stored in one of the remote credential stores 118-1to 118-N) to access external resources such as data resources in aremote storage device. As used herein, the remote storage devices mayalso be referred to as “persistent storage devices” or “shared storagedevices.” For example, the credential management system 204 may createand maintain remote credential store definitions and credential objects(e.g., in the access metadata database 206). A remote credential storedefinition identifies a remote credential store (e.g., one or more ofthe remote credential stores 118-1 to 118-N) and includes accessinformation to access security credentials from the remote credentialstore. A credential object identifies one or more security credentialsusing non-sensitive information (e.g., text strings) that are to beretrieved from a remote credential store for use in accessing anexternal resource. When a request invoking an external resource isreceived at run time, the credential management system 204 and accessmanager 202 use information stored in the access metadata database 206(e.g., a credential object and a credential store definition) toretrieve security credentials used to access the external resource froma remote credential store.

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata to process a received query (e.g., a data storage request or dataretrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214 and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 214 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor216 executes the execution code for jobs received from a queue ordetermined by the compute service manager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and then processed in that prioritized order. In anembodiment, the job scheduler and coordinator 218 determines a priorityfor internal jobs that are scheduled by the compute service manager 108with other “outside” jobs such as user queries that may be scheduled byother systems in the database (e.g., the storage platform 104) but mayutilize the same processing resources in the execution platform 110. Insome embodiments, the job scheduler and coordinator 218 identifies orassigns particular nodes in the execution platform 110 to processparticular tasks. A virtual warehouse manager 220 manages the operationof multiple virtual warehouses implemented in the execution platform110. For example, the virtual warehouse manager 220 may generate queryplans for executing received queries.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local buffers(e.g., the buffers in execution platform 110). The configuration andmetadata manager 222 uses metadata to determine which data files need tobe accessed to retrieve data for processing a particular task or job. Amonitor and workload analyzer 224 oversee processes performed by thecompute service manager 108 and manages the distribution of tasks (e.g.,workload) across the virtual warehouses and execution nodes in theexecution platform 110. The monitor and workload analyzer 224 alsoredistributes tasks, as needed, based on changing workloads throughoutthe network-based database system 102 and may further redistribute tasksbased on a user (e.g., “external”) query workload that may also beprocessed by the execution platform 110. The configuration and metadatamanager 222 and the monitor and workload analyzer 224 are coupled to adata storage device 226. Data storage device 226 in FIG. 2 representsany data storage device within the network-based database system 102.For example, data storage device 226 may represent buffers in executionplatform 110, storage devices in storage platform 104, or any otherstorage device.

As described in embodiments herein, the compute service manager 108validates all communication from an execution platform (e.g., theexecution platform 110) to validate that the content and context of thatcommunication are consistent with the task(s) known to be assigned tothe execution platform. For example, an instance of the executionplatform executing a query A should not be allowed to request access todata-source D (e.g., data storage device 226) that is not relevant toquery A. Similarly, a given execution node (e.g., execution node 302-1may need to communicate with another execution node (e.g., executionnode 302-2), and should be disallowed from communicating with a thirdexecution node (e.g., execution node 312-1) and any such illicitcommunication can be recorded (e.g., in a log or other location). Also,the information stored on a given execution node is restricted to datarelevant to the current query and any other data is unusable, renderedso by destruction or encryption where the key is unavailable.

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse n. Each virtual warehouse includesmultiple execution nodes that each include a data cache and a processor.The virtual warehouses can execute multiple tasks in parallel by usingthe multiple execution nodes. As discussed herein, the executionplatform 110 can add new virtual warehouses and drop existing virtualwarehouses in real-time based on the current processing needs of thesystems and users. This flexibility allows the execution platform 110 toquickly deploy large amounts of computing resources when needed withoutbeing forced to continue paying for those computing resources when theyare no longer needed. All virtual warehouses can access data from anydata storage device (e.g., any storage device in cloud storage platform104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 120-1 to 120-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 120-1 to120-N and, instead, can access data from any of the data storage devices120-1 to 120-N within the cloud storage platform 104. Similarly, each ofthe execution nodes shown in FIG. 3 can access data from any of the datastorage devices 120-1 to 120-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-n. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-n includes a cache 304-n and aprocessor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data being cached by the execution nodes. Forexample, these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternative embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in cloudstorage platform 104. Thus, the caches reduce or eliminate thebottleneck problems occurring in platforms that consistently retrievedata from remote storage systems. Instead of repeatedly accessing datafrom the remote storage devices, the systems and methods describedherein access data from the caches in the execution nodes, which issignificantly faster and avoids the bottleneck problem discussed above.In some embodiments, the caches are implemented using high-speed memorydevices that provide fast access to the cached data. Each cache canstore data from any of the storage devices in the cloud storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 4 is a computing environment 400 conceptually illustrating anexample software architecture, in accordance with some embodiments ofthe present disclosure.

Embodiments of subject technology model, pivot and store eachintersection of a feature/entity (column/row) value as a row in a table,discarding empty (NULL) values from the relational form for storageefficiency. The subject technology also stores the table, column and rowlevel metadata in similar format within the same table. Advantageously,this can eliminate the limitations of working in an original wide formwhere table and column limits prevent efficient storage and manipulationin a given database. Once stored in the new tall form, commonly requireddata-profiling and transformations can be applied to the featurescollectively and efficiently in a single query and pass over the data,reducing the complexity and errors that result when working with widedatasets. In an example, the techniques are enabled based at least onthe efficiency of the subject system's storage and processing forworking with data in tall semi-structured form, which is describedfurther below in the discussion of FIG. 5 and FIG. 6 .

Additionally, in some embodiments, the subject system enables supportingchanges and the storage of a Cells value over time, through additiontimestamp/version of row. Logical updates to an individual cells value,in a wide row, can be made through the insertion of a new record for therow/column coordinate, without need to modify unchanged values on therow, or touch/change the prior value. This also enables the querying ofdata AS-OF any prior reference time.

As illustrated, in embodiments, the compute service manager 108 includesan ingest engine 420, feature store database 430, cache 440, featuretransformer 450, and compiler 460.

A machine learning development environment 415 can include (not shown)several components that facilitate development of machine learningmodels. For example, in some implementations, the machine learningdevelopment environment 415 includes components that support differentusers or data scientists, a customer schema, domain specific languagepackages, a sandbox environment (e.g., data science sandbox), and thelike. It is appreciated that more or fewer components may be provided bythe machine learning development environment and still be within thescope of the subject technology.

As part of the machine learning development cycle, the aforementionedcomponents of machine learning development environment 415 canfacilitate the creation of raw input features 410, corresponding to(raw) datasets for machine learning models and development, which arethen received by the ingest engine 420 for processing.

The ingest engine 420 can ingest the raw input features on a periodicbasis for processing. The ingest engine 420 can unpivot data to afeature store form at 422. The ingest engine 420 can perform additionaloperations to convert the data to a final feature store format during afeature store stage 424 such as generating additional metadatacorresponding to table or column metadata (described further below)among other types of operations. Further, the ingest engine 420 mergesthe feature store data at 426 to the feature store database 430.

In some embodiments, as part of the machine learning model developmentcycle, the feature transformer 450 can perform one or moretransformation operations for the data (e.g., to produce one or morederived features) and then store the transformed data back into thefeature store database 430. In this manner, a lineage from the originalingestion of the raw input features 410 up to the most recenttransformation of data can be stored in the feature store database 430.

As further shown, data profiling 470 can provide functionality relatedto summarization and statistical profiling for the data in the featurestore database 430 (e.g., across tens or hundreds of features at a timebased on tags or filters). In an implementation, data profiling 470 canbe performed on a periodic basis as new or additional data is receivedor stored in the feature store database 430.

Data from the feature store database 430 can be sent (e.g., using anegress component 474 or some other communication or data transferinterface) to developers or data scientists from the machine learningdevelopment environment 415 for training such data in a given machinelearning model. For testing new input data for a trained model, datafrom feature store database 430 can be sent to one or more applications480. Such applications can include an embedded (trained) model thatperforms various predictions and other machine learning operations.

Users (e.g., data scientists) from the machine learning developmentenvironment can send API (e.g., application programming interface) orYAML (e.g., “YAML Ain′t Markup Language”) function/method calls to thecompiler 460, which may process such calls to perform the previouslydiscussed transformation operations by the feature transformer 450.Further, metadata queries 472 can be sent to the feature store database430 as part of the machine learning model development cycle.

As further shown, data from the feature store database 430 can be storedin cache 440, which can then be sent to one or more applications 480. Inan example, such data may correspond to substantially “real-time” datafor processing by a given machine learning model in the one or moreapplications 480. A table mapping features to models or applicationsenables the specific subsets of features required to be cached andmaintained, with time-variant versioning if required.

FIG. 5 illustrates examples of database tables, in accordance with someembodiments of the present disclosure.

As illustrated, a source table 510 includes data (e.g., raw inputfeatures for machine learning model development, and the like). Thesource table 510 includes multiple columns of data (e.g., column a tocolumn i in this example) in each row of the source table 510. Thus, itis appreciated that the source table 510 represents a very “wide” table(e.g., many columns per row) for storing a given dataset.

The source table 510 can be converted into a format shown in the table520 corresponding to a feature store data format. In an example, thefeature store data format converts the data from the source table 510into a tall semi-structured form (e.g., less columns or less wide thanthe table 510). The table 520, in this example, includes a column for atable identifier (ID), a row ID, a column ID, and a value for eachcorresponding column value from the source table 510.

As further shown, a table metadata table 522 for storing table metadata(e.g., timestamp for when table was created, description information,table IDs, tag strings, and the like), and a column metadata table 524for storing column metadata (e.g., timestamp for when data was ingested,data types, tag strings, table IDs, column IDs, order or ordinalposition, and the like).

The following discussion of FIG. 6 describes other examples of how thedata can be stored in the subject system in some embodiments.

FIG. 6 illustrates examples of database tables, in accordance with someembodiments of the present disclosure.

As illustrated, a source table 610 can include different input data forstoring in the subject system. The source table 610 includes multiplerows where each row includes multiple columns for different data. Inthis example the data includes data, in each row, for different vehiclesand attributes corresponding to each vehicle.

The subject system can perform an unpivot operation(s) to convert thedata from the source table 610 to a table 620. In this example, thetable 620 includes the data from the source table 610 in a single columnformat. Each row in the table 620 includes a row type, a row object ID,and data value(s) (e.g., corresponding to a cell from the source table610, table metadata, or column metadata).

In this example, the table 620 includes a row for table metadata,several rows for column metadata, and multiple rows for cell data (e.g.,corresponding to each cell from a row of the source table 610). Howeverit understood that the table 620 can include more or fewer rows andstill be within the scope of the subject technology.

Due to at least in part of this column format for storing the cell data,query operations (or other types of database operations) can be morereadily performed on logical groupings of data in the table 620. In someembodiments, logical groupings can be maintained flexibly as anadditional metadata type, within the table 620 as required, offeringflexibility in application.

FIG. 7 is a flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 700 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 700 may beperformed by components of network-based database system 102, such ascomponents of the compute service manager 108 or a node in the executionplatform 110. Accordingly, the method 700 is described below, by way ofexample with reference thereto. However, it shall be appreciated thatthe method 700 may be deployed on various other hardware configurationsand is not intended to be limited to deployment within the network-baseddatabase system 102.

At operation 702, the ingest engine 420 receives raw input data from asource table, the raw input data including data including input featuresfor a machine learning model, the raw input data being in a first formatincluding at least multiple rows with each row including multiplecolumns of values.

At operation 704, based at least in part on the source table, the ingestengine 420 generates table metadata corresponding to the source table.In an embodiment, generating table metadata includes generating a tablemetadata table, the table metadata table including a set of rows, eachrow including particular metadata, the particular metadata including atleast a timestamp for when the source table was created, descriptioninformation, table identifier, or a tag string.

At operation 706, based at least in part on the received raw input data,the ingest engine 420 generates column metadata corresponding to valuesfrom the source table. In an embodiment, generating column metadataincludes generating a column metadata table, the column metadata tableincluding a set of rows, each row including particular metadata, theparticular metadata including a timestamp for when data was ingested,data type, tag string, table identifier, column identifier, or ordinalposition.

At operation 708, the ingest engine 420 generates cell data for afeature store table based at least in part on the values from the sourcetable. In an embodiment, generating cell data includes generating a celldata table, the cell data table including a set of rows, each rowincluding particular cell data, the cell data including a tableidentifier, a row identifier, a column ID, and a value for eachcorresponding column value from the source table. Moreover, the celldata table includes a number of columns less than a number of columns ofthe source table in an example.

At operation 710, the ingest engine 420 performs at least one databaseoperation to generate the feature store table including at least thegenerated table metadata, the generated column metadata, and thegenerated cell data. In an embodiment, the at least one databaseoperation includes an unpivot operation, or a union all insertoperation. Alternatively, in an embodiment, both the unpivot operationand the union all insert operation are performed by the ingest engine420 to generate the feature store table.

In an embodiment, the feature store table includes multiple rows, eachrow including multiple columns, the multiple columns including a firstcolumn including data for a row, a second column including data for arow identifier, and a third column including data from the generatedtable metadata, the generated column metadata, and the generated celldata.

In an embodiment, the unpivot operation performs a particular operationto convert multiple columns of data from the generated table metadata,the generated column metadata, and the generated cell data into a singlerow in a single column of data in the feature store table.

In an embodiment, the union all insert operation includes combining datafrom the generated table metadata, the generated column metadata, andthe generated cell data, and inserting the combined data into thefeature store table as a one or more rows of data, the one or more rowsof data being stored in a single column of the feature store table.

Additionally, in an implementation, the feature store table includesdata stored in a feature store format, the feature store formatincluding multiple columns of data from the generated table metadata,the generated column metadata, and the generated cell data stored in asingle column in the feature store table.

FIG. 8 illustrates a diagrammatic representation of a machine 800 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 800 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 8 shows a diagrammatic representation of the machine800 in the example form of a computer system, within which instructions816 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 800 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 816 may cause the machine 800 to execute any one ormore operations of method 700. As another example, the instructions 816may cause the machine 800 to implement portions of the data flowsillustrated in at least FIG. 4 . In this way, the instructions 816transform a general, non-programmed machine into a particular machine800 (e.g., the compute service manager 108 or a node in the executionplatform 110) that is specially configured to carry out any one of thedescribed and illustrated functions in the manner described herein.

In alternative embodiments, the machine 800 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 800 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 800 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 816, sequentially orotherwise, that specify actions to be taken by the machine 800. Further,while only a single machine 800 is illustrated, the term “machine” shallalso be taken to include a collection of machines 800 that individuallyor jointly execute the instructions 816 to perform any one or more ofthe methodologies discussed herein.

The machine 800 includes processors 810, memory 830, and input/output(I/O) components 850 configured to communicate with each other such asvia a bus 802. In an example embodiment, the processors 810 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 812 and aprocessor 814 that may execute the instructions 816. The term“processor” is intended to include multi-core processors 810 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 816 contemporaneously. AlthoughFIG. 8 shows multiple processors 810, the machine 800 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 830 may include a main memory 832, a static memory 834, and astorage unit 836, all accessible to the processors 810 such as via thebus 802. The main memory 832, the static memory 834, and the storageunit 836 store the instructions 816 embodying any one or more of themethodologies or functions described herein. The instructions 816 mayalso reside, completely or partially, within the main memory 832, withinthe static memory 834, within machine storage medium 838 of the storageunit 836, within at least one of the processors 810 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 800.

The I/O components 850 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 850 thatare included in a particular machine 800 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 850 mayinclude many other components that are not shown in FIG. 8 . The I/Ocomponents 850 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 850 mayinclude output components 852 and input components 854. The outputcomponents 852 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 854 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 850 may include communication components 864 operableto couple the machine 800 to a network 880 or devices 870 via a coupling882 and a coupling 872, respectively. For example, the communicationcomponents 864 may include a network interface component or anothersuitable device to interface with the network 880. In further examples,the communication components 864 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 870 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 800 may correspond to any one of the compute service manager 108or the execution platform 110, and the devices 870 may include theclient device 114 or any other computing device described herein asbeing in communication with the network-based database system 102 or thecloud storage platform 104.

Executable Instructions and Machine Storage Medium

The various memories (e.g., 830, 832, 834, and/or memory of theprocessor(s) 810 and/or the storage unit 836) may store one or more setsof instructions 816 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 816, when executed by the processor(s) 810,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple non-transitory storage devices and/or non-transitory media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store executable instructions and/or data. The termsshall accordingly be taken to include, but not be limited to,solid-state memories, and optical and magnetic media, including memoryinternal or external to processors. Specific examples of machine-storagemedia, computer-storage media, and/or device-storage media includenon-volatile memory, including by way of example semiconductor memorydevices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM),field-programmable gate arrays (FPGAs), and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium” discussed below.

Transmission Medium

In various example embodiments, one or more portions of the network 880may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 880 or a portion of the network880 may include a wireless or cellular network, and the coupling 882 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 882 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 816 may be transmitted or received over the network 880using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components864) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions816 may be transmitted or received using a transmission medium via thecoupling 872 (e.g., a peer-to-peer coupling) to the devices 870. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 816 for execution by the machine 800, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the method 500 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

CONCLUSION

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: at least one hardwareprocessor; and a memory storing instructions that cause the at least onehardware processor to perform operations comprising: receiving, by adatabase system, raw input data from a source table provided by amachine learning development environment, the source table comprisingmultiple rows where each row includes multiple columns, the raw inputdata comprising values in a first format, the values comprising inputfeatures corresponding to datasets included in the raw input data formachine learning models, the machine learning development environmentcomprising an external system from the database system and is accessedby a plurality of different users that are external to the databasesystem; generating cell data for a feature store table based at least inpart on the values from the source table; and performing at least onedatabase operation to generate the feature store table including atleast table metadata, column metadata, and the generated cell data, thegenerated feature store table comprising a second format that causesmore efficient processing of data by the database system using a singlequery on the feature store table compared to processing the raw inputdata from the source table, the second format comprising multiplecolumns of data from the table metadata, the column metadata, and thegenerated cell data being stored in single column in the feature storetable.
 2. The system of claim 1, wherein generating cell data comprises:generating a cell data table, the cell data table including a set ofrows, each row comprising particular cell data, the cell data comprisinga table identifier, a row identifier, a column ID, and a value for eachcorresponding column value from the source table.
 3. The system of claim2, wherein the cell data table includes a number of columns less than anumber of columns of the source table.
 4. The system of claim 1, whereinthe operations further comprise: based at least in part on the sourcetable, generating table metadata corresponding to the source table; andbased at least in part on the received raw input data, generating columnmetadata corresponding to values from the source table.
 5. The system ofclaim 4, wherein generating table metadata comprises: generating a tablemetadata table, the table metadata table including a set of rows, eachrow comprising particular metadata, the particular metadata comprisingat least a timestamp for when the source table was created, descriptioninformation, table identifier, or a tag string.
 6. The system of claim4, wherein generating column metadata comprises: generating a columnmetadata table, the column metadata table including a set of rows, eachrow comprising particular metadata, the particular metadata comprising atimestamp for when data was ingested, data type, tag string, tableidentifier, column identifier, or ordinal position.
 7. The system ofclaim 1, wherein the at least one database operation comprises anunpivot operation, or a union all insert operation.
 8. The system ofclaim 7, wherein the unpivot operation performs a particular operationto convert multiple columns of data from the generated table metadata,the generated column metadata, and the generated cell data into a singlerow in a single column of data in the feature store table.
 9. The systemof claim 7, wherein the union all insert operation comprises: combiningdata from the table metadata, the column metadata, and the generatedcell data; and inserting the combined data into the feature store tableas a one or more rows of data, the one or more rows of data being storedin a single column of the feature store table.
 10. The system of claim1, wherein the feature store table comprises multiple rows, each rowcomprising multiple columns, the multiple columns including a firstcolumn including data for a row, a second column including data for arow identifier, and a third column including data from the generatedtable metadata, the generated column metadata, and the generated celldata.
 11. A method comprising: receiving, by a database system, rawinput data from a source table provided by a machine learningdevelopment environment, the source table comprising multiple rows whereeach row includes multiple columns, the raw input data comprising valuesin a first format, the values comprising input features corresponding todatasets included in the raw input data for machine learning models, themachine learning development environment comprising an external systemfrom the database system and is accessed by a plurality of differentusers that are external to the database system; generating cell data fora feature store table based at least in part on the values from thesource table; and performing at least one database operation to generatethe feature store table including at least table metadata, columnmetadata, and the generated cell data, the generated feature store tablecomprising a second format that causes more efficient processing of databy the database system using a single query on the feature store tablecompared to processing the raw input data from the source table, thesecond format comprising multiple columns of data from the tablemetadata, the column metadata, and the generated cell data being storedin single column in the feature store table.
 12. The method of claim 11,wherein generating cell data comprises: generating a cell data table,the cell data table including a set of rows, each row comprisingparticular cell data, the cell data comprising a table identifier, a rowidentifier, a column ID, and a value for each corresponding column valuefrom the source table.
 13. The method of claim 12, wherein the cell datatable includes a number of columns less than a number of columns of thesource table.
 14. The method of claim 11, further comprising: based atleast in part on the source table, generating table metadatacorresponding to the source table; and based at least in part on thereceived raw input data, generating column metadata corresponding tovalues from the source table.
 15. The method of claim 14, whereingenerating table metadata comprises: generating a table metadata table,the table metadata table including a set of rows, each row comprisingparticular metadata, the particular metadata comprising at least atimestamp for when the source table was created, descriptioninformation, table identifier, or a tag string.
 16. The method of claim14, wherein generating column metadata comprises: generating a columnmetadata table, the column metadata table including a set of rows, eachrow comprising particular metadata, the particular metadata comprising atimestamp for when data was ingested, data type, tag string, tableidentifier, column identifier, or ordinal position.
 17. The method ofclaim 11, wherein the at least one database operation comprises anunpivot operation, or a union all insert operation.
 18. The method ofclaim 17, wherein the unpivot operation performs a particular operationto convert multiple columns of data from the generated table metadata,the generated column metadata, and the generated cell data into a singlerow in a single column of data in the feature store table.
 19. Themethod of claim 17, wherein the union all insert operation comprises:combining data from the table metadata, the column metadata, and thegenerated cell data; and inserting the combined data into the featurestore table as a one or more rows of data, the one or more rows of databeing stored in a single column of the feature store table.
 20. Themethod of claim 11, wherein the feature store table comprises multiplerows, each row comprising multiple columns, the multiple columnsincluding a first column including data for a row, a second columnincluding data for a row identifier, and a third column including datafrom the generated table metadata, the generated column metadata, andthe generated cell data.
 21. A computer-storage medium comprisinginstructions that, when executed by one or more processors of a machine,configure the machine to perform operations comprising: receiving, by adatabase system, raw input data from a source table provided by amachine learning development environment, the source table comprisingmultiple rows where each row includes multiple columns, the raw inputdata comprising values in a first format, the values comprising inputfeatures corresponding to datasets included in the raw input data formachine learning models, the machine learning development environmentcomprising an external system from the database system and is accessedby a plurality of different users that are external to the databasesystem; generating cell data for a feature store table based at least inpart on the values from the source table; and performing at least onedatabase operation to generate the feature store table including atleast table metadata, column metadata, and the generated cell data, thegenerated feature store table comprising a second format that causesmore efficient processing of data by the database system using a singlequery on the feature store table compared to processing the raw inputdata from the source table, the second format comprising multiplecolumns of data from the table metadata, the column metadata, and thegenerated cell data being stored in single column in the feature storetable.
 22. The computer-storage medium of claim 21, wherein generatingcell data comprises: generating a cell data table, the cell data tableincluding a set of rows, each row comprising particular cell data, thecell data comprising a table identifier, a row identifier, a column ID,and a value for each corresponding column value from the source table.23. The computer-storage medium of claim 22, wherein the cell data tableincludes a number of columns less than a number of columns of the sourcetable.
 24. The computer-storage medium of claim 21, wherein theoperations further comprise: based at least in part on the source table,generating table metadata corresponding to the source table; and basedat least in part on the received raw input data, generating columnmetadata corresponding to values from the source table.
 25. Thecomputer-storage medium of claim 24, wherein generating table metadatacomprises: generating a table metadata table, the table metadata tableincluding a set of rows, each row comprising particular metadata, theparticular metadata comprising at least a timestamp for when the sourcetable was created, description information, table identifier, or a tagstring.
 26. The computer-storage medium of claim 24, wherein generatingcolumn metadata comprises: generating a column metadata table, thecolumn metadata table including a set of rows, each row comprisingparticular metadata, the particular metadata comprising a timestamp forwhen data was ingested, data type, tag string, table identifier, columnidentifier, or ordinal position.
 27. The computer-storage medium ofclaim 21, wherein the at least one database operation comprises anunpivot operation, or a union all insert operation.
 28. Thecomputer-storage medium of claim 27, wherein the unpivot operationperforms a particular operation to convert multiple columns of data fromthe generated table metadata, the generated column metadata, and thegenerated cell data into a single row in a single column of data in thefeature store table.
 29. The computer-storage medium of claim 27,wherein the union all insert operation comprises: combining data fromthe table metadata, the column metadata, and the generated cell data;and inserting the combined data into the feature store table as a one ormore rows of data, the one or more rows of data being stored in a singlecolumn of the feature store table.
 30. The computer-storage medium ofclaim 21, wherein the feature store table comprises multiple rows, eachrow comprising multiple columns, the multiple columns including a firstcolumn including data for a row, a second column including data for arow identifier, and a third column including data from the generatedtable metadata, the generated column metadata, and the generated celldata.